In our approach, we replace the cross-entropy loss for EBP with a triplet loss [1]}. The original formulation of EBP considers a cross-entropy loss to optimize softmax classification of a set of clases in the training set. In this new formulation, given three embeddings for a mate \((m)\) , nonmate \((n)\)  and probe \((p)\) , the triplet loss function is a max-margin hinge loss
\(L(p,n,m) = \mathtt {max}(0,~||p-m||^2 - ||p-n||^2 + \alpha ).\) 
